Spaces:
Sleeping
Sleeping
File size: 5,078 Bytes
a7cba55 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | """
RAGAS evaluation harness — the "RAGAS evaluation suite" box in the
architecture diagram (Golden dataset · 15 samples · 6 tests, F/R/P/C metrics,
Judge LLM).
Usage:
python -m evaluation.ragas_eval
python -m evaluation.ragas_eval --output results.json
"""
from __future__ import annotations
import argparse
import json
import logging
import os
from pathlib import Path
from typing import List
from dotenv import load_dotenv
load_dotenv()
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s")
logger = logging.getLogger("evaluation.ragas_eval")
GOLDEN_DATASET_PATH = Path(__file__).parent / "golden_dataset.json"
def _run_pipeline_for_eval(question: str) -> dict:
"""Runs the real LangGraph pipeline and extracts what RAGAS needs."""
import requests
# Try calling the running FastAPI server first to avoid Qdrant local file lock conflicts
api_url = os.getenv("API_URL", "http://localhost:8000")
try:
response = requests.post(
f"{api_url}/query",
json={"query": question, "thread_id": f"eval-{abs(hash(question))}"},
timeout=180
)
if response.status_code == 200:
data = response.json()
contexts = [s["text_preview"] for s in data.get("sources", [])]
return {
"answer": data.get("answer", ""),
"contexts": contexts or ["(no context retrieved)"],
}
else:
raise RuntimeError(f"API server returned status {response.status_code}: {response.text}")
except (requests.exceptions.ConnectionError, requests.exceptions.ConnectTimeout):
logger.warning("API server connection refused. Falling back to direct local execution.")
except Exception as exc:
logger.error("API query failed during evaluation: %s", exc)
raise
# Fallback to direct Python import/execution if API server is not running
from core.graph import run_query
state = run_query(question, thread_id=f"eval-{abs(hash(question))}")
contexts = [c["text"] for c in state.get("reranked_chunks", [])]
return {
"answer": state.get("final_answer", ""),
"contexts": contexts or ["(no context retrieved)"],
}
def build_evaluation_dataset(golden_samples: List[dict]):
"""Runs the pipeline for every golden question and assembles a RAGAS-ready dataset."""
from datasets import Dataset
questions, answers, contexts, ground_truths = [], [], [], []
for sample in golden_samples:
logger.info("Running pipeline for eval question: %s", sample["question"])
result = _run_pipeline_for_eval(sample["question"])
questions.append(sample["question"])
answers.append(result["answer"])
contexts.append(result["contexts"])
ground_truths.append(sample["ground_truth"])
return Dataset.from_dict(
{
"question": questions,
"answer": answers,
"contexts": contexts,
"ground_truth": ground_truths,
}
)
def run_evaluation(golden_dataset_path: Path = GOLDEN_DATASET_PATH) -> dict:
from ragas import evaluate
from ragas.metrics import (
answer_relevancy,
context_precision,
context_recall,
faithfulness,
)
golden_samples = json.loads(golden_dataset_path.read_text())
dataset = build_evaluation_dataset(golden_samples)
from langchain_groq import ChatGroq
from langchain_huggingface import HuggingFaceEmbeddings
groq_llm = ChatGroq(
model=os.getenv("GROQ_PRIMARY_MODEL", "llama-3.3-70b-versatile"),
api_key=os.getenv("GROQ_API_KEY"),
)
embeddings = HuggingFaceEmbeddings(
model_name=os.getenv("EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
)
from ragas.run_config import RunConfig
rate_friendly_config = RunConfig(
max_workers=2,
max_retries=20,
timeout=180
)
logger.info("Running RAGAS metrics: faithfulness, answer_relevancy, context_precision, context_recall")
result = evaluate(
dataset,
metrics=[faithfulness, answer_relevancy, context_precision, context_recall],
llm=groq_llm,
embeddings=embeddings,
run_config=rate_friendly_config,
)
scores = result.to_pandas().mean(numeric_only=True).to_dict()
logger.info("RAGAS results: %s", scores)
return scores
def main() -> None:
parser = argparse.ArgumentParser(description="Run RAGAS evaluation against the golden dataset.")
parser.add_argument("--dataset", type=str, default=str(GOLDEN_DATASET_PATH))
parser.add_argument("--output", type=str, default="evaluation/results.json")
args = parser.parse_args()
scores = run_evaluation(Path(args.dataset))
output_path = Path(args.output)
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(json.dumps(scores, indent=2))
logger.info("Wrote evaluation results to %s", output_path)
if __name__ == "__main__":
main()
|